Backetball Strategies with GAIL and BatchPPO

please go APPENDIX – B Defensive Strategies Generation by Imitation Learning for this project.

Getting Started

Clone this repo:

$ git clone https://github.com/chychen/RL_strategies

Environment Setting docker/ nvidia-docker:

$ docker pull jaycase/bballgail:init
$ nvidia-docker run --name {name} -it -p 127.0.0.1:6006:6006 -v {repo_path}RL_strategies/:/RL_strategies -w /RL_strategies jaycase/bballgail bash

Download the dataset. (You should put dataset right under the folder "{repo_path}/RL_strategies/data/") (QQ can't find the files, maybe you could try this one)

root@7bdb7335cde0:/RL_strategies# cd bball_strategies/data/
root@7bdb7335cde0:/RL_strategies/bball_strategies/data# wget http://140.113.210.14:6006/NBA/data/FPS5.npy
root@7bdb7335cde0:/RL_strategies/bball_strategies/data# wget http://140.113.210.14:6006/NBA/data/FPS5Length.npy

Data Preprocessing

Filter out bad data.

root@7bdb7335cde0:/RL_strategies/bball_strategies/data# python3 preprocess.py

Create training dataset for GAIL. (transform data into state-action pair.)

root@7bdb7335cde0:/RL_strategies/bball_strategies/data# python3 create_gail_data.py

Reorder the data offense and defense positions by rule-based method.
And, duplicate OrderedGAILTransitionData_52.hdf5 for multi process settings.

root@7bdb7335cde0:/RL_strategies/bball_strategies/data# python3 postprocess_data_order.py 
root@7bdb7335cde0:/RL_strategies/bball_strategies/data# cp OrderedGAILTransitionData_52.hdf5 OrderedGAILTransitionData_522.hdf5

Training

Configuration

All configurations are in "{repo_path}/RL_strategies/bball_strategies/scripts/gail/config.py"

Train Model

root@7bdb7335cde0:/RL_strategies# python3 -m bball_strategies.scripts.gail.train --config=double_curiculum

Monitor training

root@7bdb7335cde0:/RL_strategies# tensorboard --logdir='logdir/gail_defense/{time stamp}-double_curiculum' --port=6006

Analysis

###Custimized Animation Players

Buid upon kivy framework
You can drag and drop the '.npz' files (recorded while tranining) into the player.

root@7bdb7335cde0:/RL_strategies# cd gui_tool/
root@7bdb7335cde0:/RL_strategies/gui_tool# python3 player.py

Quantitative Analysis

Visualize results by plotly framework

root@7bdb7335cde0:/RL_strategies# cd analysis/
root@7bdb7335cde0:/RL_strategies/analysis# python3 evaluation.py

Acknowledgement

Basically, the code is built upon the TensorFlow Agents Framework.

@article{hafner2017agents,
  title={TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow},
  author={Hafner, Danijar and Davidson, James and Vanhoucke, Vincent},
  journal={arXiv preprint arXiv:1709.02878},
  year={2017}
}

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rl_strategies's Introduction

Backetball Strategies with GAIL and BatchPPO

Getting Started

Data Preprocessing

Training

Configuration

Train Model

Monitor training

Analysis

Quantitative Analysis

Acknowledgement

Basically, the code is built upon the TensorFlow Agents Framework.

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